On the interpretation of seasonal Southern Africa precipitation prediction skill estimates during Austral summer
Differences between two types of prediction skill estimates over Southern Africa are illustrated to better inform the users of seasonal precipitation forecasts over the region who desire assessments of forecast accuracy. Both seasonal precipitation prediction skill estimates for the African continent south of 15°S during the December–March rainy season are derived from the perfect-model method. The perfect-model method is based on a 40-member ensemble of Community Atmosphere Model version 5 simulations forced by observed time-evolving boundary conditions during 1920–2016. The first skill estimate is based on the verification of an ensemble mean forecast spanning many seasons and therefore unconditional on a single boundary forcing. The second skill estimate is based on the verification of an ensemble mean forecast for a single season and is therefore conditional on that year’s boundary forcing. Unconditional prediction skill calculated in 30-year increments for each of the 40 possible forecasts reveals: (1) large spread in skill among the individual forecasts for any given year and (2) temporal variations in skill for each forecast. The magnitude of conditional prediction skill varies greatly from 1 year to the next, revealing that the boundary conditions offer little prediction skill during some years and comparably large skill during others. The simultaneous behaviors of the El Niño–Southern Oscillation and the subtropical Indian Ocean Dipole are related to the largest conditional precipitation prediction skill years. Unconditional skill estimates may therefore mislead users of forecasts who desire assessments of forecast accuracy. Unconditional skill may be temporally unstable, and unlike conditional skill, is not representative of the skill for a given season.
The authors are grateful for support from the Famine Early Warning Systems Network and constructive comments from Tom Hamill and two reviewers.
- Dixon J, Gulliver A, Gibbon D (2001) Farming systems and poverty: improving farmers’ livelihoods in a changing world. FAO and World Bank, Rome, p 407Google Scholar
- Neale R et al (2012) Description of the NCAR Community Atmosphere Model (CAM 5.0). NCAR Tech. Note NCAR/TN-486+STR, National Center for Atmospheric Research, p 289. http://www.cesm.ucar.edu/models/cesm1.0/cam/docs/description/cam5_desc.pdf
- Palmer TN, Anderson DLT (1994) The prospects for seasonal forecasting—a review paper. Q J R Meteorol Soc 120:755–793Google Scholar
- Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, Kent EC, Kaplan A (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res 108(D14):4407. https://doi.org/10.1029/2002JD002670 CrossRefGoogle Scholar
- Sarewitz D, Pielke RA Jr, Byerly R Jr (2000) Prediction: science, decision making, and the future of nature. Island Press, Covelo, p 405Google Scholar
- Tanre D, Geleyn J-F, Slingo JM (1984) First results of the introduction of an advanced aerosol-radiation interaction in the ECMWF low resolution global model. In: Gerber HE, Deepak A (eds) Aerosols and their climatic effects. Deepak Publishing, Hampton, VA, USA, pp 133–177Google Scholar